(1) Field of the Invention
The present invention relates to transducers, and more specifically to an acoustic wave transducer that functions based on the same transduction principles found in cicadas, designed by means of efficient computation of the higher order (i.e., nonlinear) kernels in a Volterra or Wiener expansion used to validate the transducer model.
(2) Description of the Prior Art
Cicadas emit one of the loudest sounds in all of the insect population despite their relatively small size. A cicada's sound production system allows for propagation distances of approximately one quarter of a mile for the periodic cicada and beyond a mile for some annual cicadas. The sound level for some species is over 120 dB relative to (the intensity of a plane wave of) pressure equal to 20 micro-Pascals. This represents an exceptional transmission distance for the size of the sound production system. The cicada's highly effective sound-production system occupies a physical space typically less than 3 cubic centimeters. Males create sound by flexing a pair of ridged abdominal membranes called tymbals. The cicada uses its tymbal muscle to pull the tymbal, which causes the tymbal ribs to buckle releasing sound impulses. The sounds made by these tymbals are amplified by the hollow abdomen functioning as a tuned resonator. The cicada song has been classically modeled using linear mathematical methods. Unfortunately, these linear methods are insufficient for a true model of the system because the non-elastic (i.e., nonlinear) buckling tymbals of the cicada sound production system are essential to the acoustic level and propagation of the sound. The present invention is a method and apparatus that emulates the cicada sound production system. This bio-inspired method and apparatus potentially provides a precision method for improved detection, classification and generation of acoustic signals in air and in water.
Most acoustic signal processing methods in use today are based on a first order (linear) kernel estimation. Whenever higher order kernels exist in physical systems, these kernels will masquerade as noise in a first order approximation. By uncovering the higher order kernels in physical systems, new possibilities exist for achieving significant computational gains in receiver signal-to-background interference levels not possible using linear methods. Moreover, the signal content of these higher order kernels, once detected, can provide new and useful information about an acoustic signal source.
Previous work in acoustic signal processing has demonstrated a utility in the application of the Volterra series expansion and other nonlinear methods for the exploitation of signals via application of a Volterra and/or Wiener signal processing procedure to measure and quantify higher-order non-linearities. The present invention teaches a signal processing breakthrough that significantly alleviates the “Curse of Dimensionality” (COD) in the characterization of nonlinear physical systems; namely, the reduction in the number of coefficients used to describe the higher order (i.e., nonlinear) kernels in the Volterra series expansion used to validate the finite element (FE) model that is instrumental in the development of the transducer model. The latter technique provides the means to evaluate simultaneously from a wide band excitation, all the inter-modulation products up to a specified order by greatly reducing the number of coefficients in the higher order kernel estimation to a manageable set that can be easily manipulated by current personal computers.